Overview

Dataset statistics

Number of variables18
Number of observations22730
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory144.0 B

Variable types

Numeric13
Categorical5

Alerts

squareMeters is highly overall correlated with priceHigh correlation
price is highly overall correlated with squareMetersHigh correlation
squareMeters is highly skewed (γ1 = 79.25331443)Skewed
floors is highly skewed (γ1 = 85.12232772)Skewed
made is highly skewed (γ1 = 66.93441091)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
hasGuestRoom has 1970 (8.7%) zerosZeros

Reproduction

Analysis started2023-02-16 09:35:51.981509
Analysis finished2023-02-16 09:36:42.254863
Duration50.27 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct22730
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11364.5
Minimum0
Maximum22729
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:42.420866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1136.45
Q15682.25
median11364.5
Q317046.75
95-th percentile21592.55
Maximum22729
Range22729
Interquartile range (IQR)11364.5

Descriptive statistics

Standard deviation6561.7301
Coefficient of variation (CV)0.57738837
Kurtosis-1.2
Mean11364.5
Median Absolute Deviation (MAD)5682.5
Skewness0
Sum2.5831508 × 108
Variance43056302
MonotonicityStrictly increasing
2023-02-16T15:06:42.681865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
15182 1
 
< 0.1%
15158 1
 
< 0.1%
15157 1
 
< 0.1%
15156 1
 
< 0.1%
15155 1
 
< 0.1%
15154 1
 
< 0.1%
15153 1
 
< 0.1%
15152 1
 
< 0.1%
15151 1
 
< 0.1%
Other values (22720) 22720
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
22729 1
< 0.1%
22728 1
< 0.1%
22727 1
< 0.1%
22726 1
< 0.1%
22725 1
< 0.1%
22724 1
< 0.1%
22723 1
< 0.1%
22722 1
< 0.1%
22721 1
< 0.1%
22720 1
< 0.1%

squareMeters
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7319
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46586.218
Minimum89
Maximum6071330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:42.950781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile3760
Q120392.75
median44484
Q371547
95-th percentile93656
Maximum6071330
Range6071241
Interquartile range (IQR)51154.25

Descriptive statistics

Standard deviation49521.245
Coefficient of variation (CV)1.063002
Kurtosis9638.226
Mean46586.218
Median Absolute Deviation (MAD)25742
Skewness79.253314
Sum1.0589047 × 109
Variance2.4523537 × 109
MonotonicityNot monotonic
2023-02-16T15:06:43.203243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
925 19
 
0.1%
54369 17
 
0.1%
85184 17
 
0.1%
965 17
 
0.1%
44698 17
 
0.1%
46367 17
 
0.1%
36417 17
 
0.1%
25051 16
 
0.1%
4187 16
 
0.1%
8719 16
 
0.1%
Other values (7309) 22561
99.3%
ValueCountFrequency (%)
89 12
0.1%
111 1
 
< 0.1%
123 4
 
< 0.1%
137 4
 
< 0.1%
141 6
< 0.1%
149 5
< 0.1%
152 7
< 0.1%
153 2
 
< 0.1%
163 5
< 0.1%
176 2
 
< 0.1%
ValueCountFrequency (%)
6071330 1
 
< 0.1%
146181 1
 
< 0.1%
99985 4
< 0.1%
99952 3
< 0.1%
99932 1
 
< 0.1%
99913 4
< 0.1%
99886 1
 
< 0.1%
99885 4
< 0.1%
99854 1
 
< 0.1%
99820 5
< 0.1%

numberOfRooms
Real number (ℝ)

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.241091
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:43.471599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q125
median47
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.226428
Coefficient of variation (CV)0.58511173
Kurtosis-1.1420077
Mean48.241091
Median Absolute Deviation (MAD)23
Skewness0.11507951
Sum1096520
Variance796.73126
MonotonicityNot monotonic
2023-02-16T15:06:43.748302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 935
 
4.1%
38 855
 
3.8%
86 642
 
2.8%
35 604
 
2.7%
58 581
 
2.6%
8 529
 
2.3%
49 481
 
2.1%
85 453
 
2.0%
84 384
 
1.7%
50 380
 
1.7%
Other values (90) 16886
74.3%
ValueCountFrequency (%)
1 126
 
0.6%
2 133
 
0.6%
3 168
 
0.7%
4 379
1.7%
5 299
1.3%
6 370
1.6%
7 234
1.0%
8 529
2.3%
9 142
 
0.6%
10 167
 
0.7%
ValueCountFrequency (%)
100 213
0.9%
99 300
1.3%
98 242
1.1%
97 140
0.6%
96 123
0.5%
95 138
0.6%
94 102
 
0.4%
93 53
 
0.2%
92 91
 
0.4%
91 67
 
0.3%

hasYard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size177.7 KiB
0
11913 
1
10817 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

Length

2023-02-16T15:06:43.951046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-16T15:06:44.266891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

Most occurring characters

ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common 22730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11913
52.4%
1 10817
47.6%

hasPool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size177.7 KiB
0
12439 
1
10291 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

Length

2023-02-16T15:06:44.405878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-16T15:06:44.584667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

Most occurring characters

ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 22730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12439
54.7%
1 10291
45.3%

floors
Real number (ℝ)

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.305983
Minimum1
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:44.778276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q125
median45
Q369
95-th percentile91
Maximum6000
Range5999
Interquartile range (IQR)44

Descriptive statistics

Standard deviation47.777207
Coefficient of variation (CV)1.0099612
Kurtosis10602.073
Mean47.305983
Median Absolute Deviation (MAD)22
Skewness85.122328
Sum1075265
Variance2282.6615
MonotonicityNot monotonic
2023-02-16T15:06:45.004795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 792
 
3.5%
38 690
 
3.0%
50 657
 
2.9%
37 482
 
2.1%
49 470
 
2.1%
55 467
 
2.1%
41 456
 
2.0%
26 450
 
2.0%
8 443
 
1.9%
64 435
 
1.9%
Other values (91) 17388
76.5%
ValueCountFrequency (%)
1 163
 
0.7%
2 112
 
0.5%
3 133
 
0.6%
4 250
1.1%
5 367
1.6%
6 308
1.4%
7 282
1.2%
8 443
1.9%
9 223
1.0%
10 120
 
0.5%
ValueCountFrequency (%)
6000 1
 
< 0.1%
100 175
0.8%
99 193
0.8%
98 217
1.0%
97 106
0.5%
96 106
0.5%
95 136
0.6%
94 78
 
0.3%
93 37
 
0.2%
92 31
 
0.1%

cityCode
Real number (ℝ)

Distinct7810
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50013.796
Minimum3
Maximum491100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:45.231749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4151
Q122936
median50414
Q376291
95-th percentile95466
Maximum491100
Range491097
Interquartile range (IQR)53355

Descriptive statistics

Standard deviation30006.638
Coefficient of variation (CV)0.59996721
Kurtosis2.4418052
Mean50013.796
Median Absolute Deviation (MAD)26632
Skewness0.2457956
Sum1.1368136 × 109
Variance9.0039831 × 108
MonotonicityNot monotonic
2023-02-16T15:06:45.696499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1906 69
 
0.3%
42634 35
 
0.2%
42626 35
 
0.2%
3545 32
 
0.1%
426 31
 
0.1%
26389 30
 
0.1%
42653 29
 
0.1%
78116 28
 
0.1%
56267 26
 
0.1%
97769 24
 
0.1%
Other values (7800) 22391
98.5%
ValueCountFrequency (%)
3 2
 
< 0.1%
5 3
< 0.1%
24 4
< 0.1%
34 1
 
< 0.1%
38 3
< 0.1%
59 6
< 0.1%
63 1
 
< 0.1%
101 2
 
< 0.1%
120 7
< 0.1%
135 4
< 0.1%
ValueCountFrequency (%)
491100 1
 
< 0.1%
465360 1
 
< 0.1%
201035 1
 
< 0.1%
200812 1
 
< 0.1%
200801 2
 
< 0.1%
146275 1
 
< 0.1%
99953 1
 
< 0.1%
99940 1
 
< 0.1%
99921 10
< 0.1%
99905 3
 
< 0.1%

cityPartRange
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5850418
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:45.906998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7395333
Coefficient of variation (CV)0.49051259
Kurtosis-1.1500556
Mean5.5850418
Median Absolute Deviation (MAD)2
Skewness-0.093055751
Sum126948
Variance7.5050426
MonotonicityNot monotonic
2023-02-16T15:06:46.068758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 3255
14.3%
5 2957
13.0%
2 2262
10.0%
7 2237
9.8%
9 2224
9.8%
4 2169
9.5%
6 2134
9.4%
3 1925
8.5%
1 1889
8.3%
10 1678
7.4%
ValueCountFrequency (%)
1 1889
8.3%
2 2262
10.0%
3 1925
8.5%
4 2169
9.5%
5 2957
13.0%
6 2134
9.4%
7 2237
9.8%
8 3255
14.3%
9 2224
9.8%
10 1678
7.4%
ValueCountFrequency (%)
10 1678
7.4%
9 2224
9.8%
8 3255
14.3%
7 2237
9.8%
6 2134
9.4%
5 2957
13.0%
4 2169
9.5%
3 1925
8.5%
2 2262
10.0%
1 1889
8.3%

numPrevOwners
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6207655
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:46.781266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7130257
Coefficient of variation (CV)0.48267904
Kurtosis-1.10883
Mean5.6207655
Median Absolute Deviation (MAD)2
Skewness-0.077110476
Sum127760
Variance7.3605085
MonotonicityNot monotonic
2023-02-16T15:06:46.969330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 2868
12.6%
8 2639
11.6%
9 2458
10.8%
7 2437
10.7%
4 2376
10.5%
6 2363
10.4%
3 2065
9.1%
2 1985
8.7%
1 1790
7.9%
10 1749
7.7%
ValueCountFrequency (%)
1 1790
7.9%
2 1985
8.7%
3 2065
9.1%
4 2376
10.5%
5 2868
12.6%
6 2363
10.4%
7 2437
10.7%
8 2639
11.6%
9 2458
10.8%
10 1749
7.7%
ValueCountFrequency (%)
10 1749
7.7%
9 2458
10.8%
8 2639
11.6%
7 2437
10.7%
6 2363
10.4%
5 2868
12.6%
4 2376
10.5%
3 2065
9.1%
2 1985
8.7%
1 1790
7.9%

made
Real number (ℝ)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.0051
Minimum1990
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:47.173672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1994
Q12000
median2006
Q32014
95-th percentile2019
Maximum10000
Range8010
Interquartile range (IQR)14

Descriptive statistics

Standard deviation118.82678
Coefficient of variation (CV)0.059176533
Kurtosis4499.6461
Mean2008.0051
Median Absolute Deviation (MAD)7
Skewness66.934411
Sum45641955
Variance14119.803
MonotonicityNot monotonic
2023-02-16T15:06:47.807626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2000 3588
 
15.8%
2003 1156
 
5.1%
2014 1070
 
4.7%
2015 1036
 
4.6%
2007 1021
 
4.5%
2008 981
 
4.3%
2009 972
 
4.3%
2019 956
 
4.2%
2013 934
 
4.1%
2018 927
 
4.1%
Other values (23) 10089
44.4%
ValueCountFrequency (%)
1990 77
 
0.3%
1991 30
 
0.1%
1992 59
 
0.3%
1993 651
2.9%
1994 628
2.8%
1995 631
2.8%
1996 822
3.6%
1997 592
2.6%
1998 719
3.2%
1999 588
2.6%
ValueCountFrequency (%)
10000 5
 
< 0.1%
2021 110
 
0.5%
2020 595
2.6%
2019 956
4.2%
2018 927
4.1%
2017 741
3.3%
2016 910
4.0%
2015 1036
4.6%
2014 1070
4.7%
2013 934
4.1%

isNewBuilt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size177.7 KiB
0
12093 
1
10637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%

Length

2023-02-16T15:06:48.021666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-16T15:06:48.188566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%

Most occurring characters

ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common 22730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12093
53.2%
1 10637
46.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size177.7 KiB
0
12274 
1
10456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

Length

2023-02-16T15:06:48.350507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-16T15:06:48.512121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

Most occurring characters

ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12274
54.0%
1 10456
46.0%

basement
Real number (ℝ)

Distinct4903
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5283.6466
Minimum4
Maximum91992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:48.684267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile683.35
Q12977.25
median5359
Q37746
95-th percentile9477.55
Maximum91992
Range91988
Interquartile range (IQR)4768.75

Descriptive statistics

Standard deviation3047.0844
Coefficient of variation (CV)0.57670102
Kurtosis93.568049
Mean5283.6466
Median Absolute Deviation (MAD)2386
Skewness3.3366368
Sum1.2009729 × 108
Variance9284723.4
MonotonicityNot monotonic
2023-02-16T15:06:48.919282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874 96
 
0.4%
705 93
 
0.4%
4173 93
 
0.4%
8181 91
 
0.4%
783 82
 
0.4%
7739 68
 
0.3%
5359 61
 
0.3%
8174 61
 
0.3%
5275 60
 
0.3%
7253 58
 
0.3%
Other values (4893) 21967
96.6%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 2
 
< 0.1%
8 19
0.1%
10 5
 
< 0.1%
11 1
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
16 9
< 0.1%
17 10
< 0.1%
23 3
 
< 0.1%
ValueCountFrequency (%)
91992 1
 
< 0.1%
91978 1
 
< 0.1%
84333 1
 
< 0.1%
81851 1
 
< 0.1%
10000 18
0.1%
9997 1
 
< 0.1%
9989 1
 
< 0.1%
9988 5
 
< 0.1%
9985 5
 
< 0.1%
9984 2
 
< 0.1%

attic
Real number (ℝ)

Distinct5167
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5067.9817
Minimum1
Maximum96381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:49.137866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile490
Q12599
median4977
Q37652
95-th percentile9433.5
Maximum96381
Range96380
Interquartile range (IQR)5053

Descriptive statistics

Standard deviation3097.3479
Coefficient of variation (CV)0.61116005
Kurtosis68.698849
Mean5067.9817
Median Absolute Deviation (MAD)2538
Skewness2.8099634
Sum1.1519522 × 108
Variance9593564.3
MonotonicityNot monotonic
2023-02-16T15:06:49.380612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4926 82
 
0.4%
9057 56
 
0.2%
7497 49
 
0.2%
989 46
 
0.2%
4910 46
 
0.2%
9188 44
 
0.2%
4146 42
 
0.2%
973 41
 
0.2%
8794 40
 
0.2%
779 38
 
0.2%
Other values (5157) 22246
97.9%
ValueCountFrequency (%)
1 3
 
< 0.1%
4 12
0.1%
5 3
 
< 0.1%
7 10
< 0.1%
8 23
0.1%
9 1
 
< 0.1%
10 12
0.1%
11 2
 
< 0.1%
12 5
 
< 0.1%
13 5
 
< 0.1%
ValueCountFrequency (%)
96381 1
 
< 0.1%
71965 1
 
< 0.1%
71024 1
 
< 0.1%
71001 2
 
< 0.1%
30000 2
 
< 0.1%
10000 8
< 0.1%
9999 5
< 0.1%
9994 2
 
< 0.1%
9988 3
 
< 0.1%
9985 6
< 0.1%

garage
Real number (ℝ)

Distinct896
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530.46964
Minimum4
Maximum9017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:49.617557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile125
Q1297
median515
Q3767
95-th percentile958
Maximum9017
Range9013
Interquartile range (IQR)470

Descriptive statistics

Standard deviation274.8406
Coefficient of variation (CV)0.51810807
Kurtosis38.674698
Mean530.46964
Median Absolute Deviation (MAD)238
Skewness1.3787588
Sum12057575
Variance75537.357
MonotonicityNot monotonic
2023-02-16T15:06:49.828969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
377 222
 
1.0%
414 154
 
0.7%
404 127
 
0.6%
976 124
 
0.5%
308 120
 
0.5%
127 115
 
0.5%
424 109
 
0.5%
269 102
 
0.4%
775 101
 
0.4%
905 98
 
0.4%
Other values (886) 21458
94.4%
ValueCountFrequency (%)
4 2
 
< 0.1%
8 1
 
< 0.1%
100 8
 
< 0.1%
101 89
0.4%
102 4
 
< 0.1%
103 8
 
< 0.1%
104 65
0.3%
105 21
 
0.1%
106 43
0.2%
107 8
 
< 0.1%
ValueCountFrequency (%)
9017 1
 
< 0.1%
2048 1
 
< 0.1%
1000 88
0.4%
999 37
0.2%
998 34
 
0.1%
997 30
 
0.1%
996 4
 
< 0.1%
995 14
 
0.1%
994 19
 
0.1%
993 14
 
0.1%

hasStorageRoom
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size177.7 KiB
0
12236 
1
10494 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

Length

2023-02-16T15:06:50.055381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-16T15:06:50.273789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

Most occurring characters

ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 22730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12236
53.8%
1 10494
46.2%

hasGuestRoom
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1530576
Minimum0
Maximum10
Zeros1970
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:50.437582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0552462
Coefficient of variation (CV)0.59289967
Kurtosis-1.1432721
Mean5.1530576
Median Absolute Deviation (MAD)3
Skewness-0.11777178
Sum117129
Variance9.3345291
MonotonicityNot monotonic
2023-02-16T15:06:50.640833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 2970
13.1%
4 2277
10.0%
5 2187
9.6%
7 2137
9.4%
6 2116
9.3%
2 2018
8.9%
0 1970
8.7%
9 1911
8.4%
3 1897
8.3%
10 1722
7.6%
ValueCountFrequency (%)
0 1970
8.7%
1 1525
6.7%
2 2018
8.9%
3 1897
8.3%
4 2277
10.0%
5 2187
9.6%
6 2116
9.3%
7 2137
9.4%
8 2970
13.1%
9 1911
8.4%
ValueCountFrequency (%)
10 1722
7.6%
9 1911
8.4%
8 2970
13.1%
7 2137
9.4%
6 2116
9.3%
5 2187
9.6%
4 2277
10.0%
3 1897
8.3%
2 2018
8.9%
1 1525
6.7%

price
Real number (ℝ)

Distinct7421
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4634456.9
Minimum10313.5
Maximum10004278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size177.7 KiB
2023-02-16T15:06:50.885939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10313.5
5-th percentile369764.1
Q12041739.1
median4450823.4
Q37159919.5
95-th percentile9369720.7
Maximum10004278
Range9993964.8
Interquartile range (IQR)5118180.4

Descriptive statistics

Standard deviation2925163.2
Coefficient of variation (CV)0.63117714
Kurtosis-1.2038013
Mean4634456.9
Median Absolute Deviation (MAD)2574827.7
Skewness0.13139962
Sum1.0534121 × 1011
Variance8.55658 × 1012
MonotonicityNot monotonic
2023-02-16T15:06:51.266153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8522151.8 18
 
0.1%
107936.9 18
 
0.1%
362218.9 16
 
0.1%
2506485.7 16
 
0.1%
4473490.3 16
 
0.1%
6031090.9 16
 
0.1%
4641159.4 15
 
0.1%
6684800.8 15
 
0.1%
4590725.6 15
 
0.1%
5384554.6 15
 
0.1%
Other values (7411) 22570
99.3%
ValueCountFrequency (%)
10313.5 12
0.1%
13229.1 4
 
< 0.1%
14431.3 1
 
< 0.1%
16799.2 1
 
< 0.1%
17363 3
 
< 0.1%
18985 1
 
< 0.1%
19638 1
 
< 0.1%
19788.5 2
 
< 0.1%
21201.1 4
 
< 0.1%
22160.2 7
< 0.1%
ValueCountFrequency (%)
10004278.3 4
< 0.1%
10002945 1
 
< 0.1%
9999687.3 1
 
< 0.1%
9998411 2
 
< 0.1%
9998090.4 4
< 0.1%
9994093.4 1
 
< 0.1%
9989598.9 1
 
< 0.1%
9989210.1 4
< 0.1%
9988416.5 2
 
< 0.1%
9987501.4 5
< 0.1%

Interactions

2023-02-16T15:06:38.703534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:03.253596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:06.509515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:09.508183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:12.457425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:15.141920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:17.501819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.966813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:26.278360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:28.694939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.048654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:33.653251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:36.095962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:38.865856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:03.676591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:06.755813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:09.756344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:12.640766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:15.336678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:17.679652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:24.158054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:26.468017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:28.862478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.210748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:33.832820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:36.259720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.053647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:03.927314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:06.973624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:09.972876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:12.843070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:15.524763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:17.900956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:24.347097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:26.663760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.042581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.413357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:34.056767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:36.455756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.225270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:04.189457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:07.173352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:10.238542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:13.043607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:15.713703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:18.090157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:24.524323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:26.858124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.220310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.584615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:34.220063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:36.626602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.390689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:04.410538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:07.376930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:10.508228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:13.249645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:15.931906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:18.290184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:24.719932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.051082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.385372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.757808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:34.412613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:36.837432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.558995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:04.620033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:07.610022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:10.773247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:13.441786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.125445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:18.460595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:24.890406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.243134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.607671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:31.924275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:34.634697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:37.009345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.732505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:04.809450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:07.876531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:10.977672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:13.654266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.304262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:18.629344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.050863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.432474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.769763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.098707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:34.830319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:37.406601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:39.906208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:04.974199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:08.198248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:11.180611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:13.840437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.465021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:18.784912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.213856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.601268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:29.923993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.263373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.000469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:37.607955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:40.105390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:05.210614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:08.441346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:11.385358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:14.068377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.647776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.095688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.398742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.797763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:30.138598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.449746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.179474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:37.800967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:40.288331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:05.430979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:08.678902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:11.582812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:14.248001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.820194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.276181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.569971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:27.968669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:30.318427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.610990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.343016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:38.005328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:40.449023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:05.723834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:08.871072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:11.842164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:14.459783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:16.985096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.457457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.740687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:28.156229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:30.484235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.780377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.520541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:38.188591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:40.626423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:05.974969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:09.053642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:12.045529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:14.674952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:17.162753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.622025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:25.918235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:28.334074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:30.670096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:32.949211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.734085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:38.359453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:40.836935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:06.232414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:09.258686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:12.249395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:14.908511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:17.335573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:23.792455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:26.079037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:28.505383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:30.856081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:33.113520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:35.924352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-16T15:06:38.525479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-16T15:06:51.584739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
idsquareMetersnumberOfRoomsfloorscityCodecityPartRangenumPrevOwnersmadebasementatticgaragehasGuestRoompricehasYardhasPoolisNewBuilthasStormProtectorhasStorageRoom
id1.000-0.0080.004-0.0120.0090.013-0.0010.0100.0030.0020.0100.003-0.0080.0160.0000.0090.0000.023
squareMeters-0.0081.0000.0910.0590.022-0.009-0.0070.131-0.037-0.008-0.121-0.0100.9980.0000.0000.0000.0000.000
numberOfRooms0.0040.0911.0000.076-0.0070.0120.021-0.0330.0240.0250.065-0.0160.0910.0130.0210.0070.0180.000
floors-0.0120.0590.0761.0000.0030.0080.018-0.0560.0090.0030.035-0.0320.0600.0000.0000.0000.0000.000
cityCode0.0090.022-0.0070.0031.0000.009-0.0050.0150.0030.0180.0060.0030.0210.0000.0000.0090.0000.000
cityPartRange0.013-0.0090.0120.0080.0091.0000.0210.008-0.0000.008-0.0010.018-0.0090.0000.0000.0010.0160.015
numPrevOwners-0.001-0.0070.0210.018-0.0050.0211.0000.016-0.0060.003-0.0050.013-0.0080.0160.0130.0000.0220.000
made0.0100.131-0.033-0.0560.0150.0080.0161.0000.0330.0260.0320.0220.1310.0000.0000.0000.0000.000
basement0.003-0.0370.0240.0090.003-0.000-0.0060.0331.0000.020-0.0180.018-0.0370.0000.0100.0090.0070.001
attic0.002-0.0080.0250.0030.0180.0080.0030.0260.0201.000-0.0320.014-0.0080.0000.0040.0050.0160.007
garage0.010-0.1210.0650.0350.006-0.001-0.0050.032-0.018-0.0321.0000.007-0.1220.0000.0000.0070.0100.000
hasGuestRoom0.003-0.010-0.016-0.0320.0030.0180.0130.0220.0180.0140.0071.000-0.0100.0110.0140.0000.0200.000
price-0.0080.9980.0910.0600.021-0.009-0.0080.131-0.037-0.008-0.122-0.0101.0000.0310.0330.0210.0200.015
hasYard0.0160.0000.0130.0000.0000.0000.0160.0000.0000.0000.0000.0110.0311.0000.0680.0000.0000.000
hasPool0.0000.0000.0210.0000.0000.0000.0130.0000.0100.0040.0000.0140.0330.0681.0000.0320.0130.015
isNewBuilt0.0090.0000.0070.0000.0090.0010.0000.0000.0090.0050.0070.0000.0210.0000.0321.0000.0230.027
hasStormProtector0.0000.0000.0180.0000.0000.0160.0220.0000.0070.0160.0100.0200.0200.0000.0130.0231.0000.016
hasStorageRoom0.0230.0000.0000.0000.0000.0150.0000.0000.0010.0070.0000.0000.0150.0000.0150.0270.0161.000

Missing values

2023-02-16T15:06:41.119425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-16T15:06:41.697473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idsquareMetersnumberOfRoomshasYardhasPoolfloorscityCodecityPartRangenumPrevOwnersmadeisNewBuilthasStormProtectorbasementatticgaragehasStorageRoomhasGuestRoomprice
0034291241047356932120000185196369033436795.2
119514560016034773142000017294496277069519958.0
2292661451162454574820201174738953245199276448.1
3397184990059151131120000164248522256199725732.2
44617521000057642458420181071512786863076181908.8
5530300360035190698419981081811826589003033117.8
6619341951037239399620170071739233101031944096.7
77581103101676588420031031643389499155814953.3
8820537840144956031019940050267669191092056267.4
9989396351196339286719930079362956414018949480.3
idsquareMetersnumberOfRoomshasYardhasPoolfloorscityCodecityPartRangenumPrevOwnersmadeisNewBuilthasStormProtectorbasementatticgaragehasStorageRoomhasGuestRoomprice
22720227206088617005287355420050145109402146026090272.5
227212272110919671084781169419940119201906558071100481.4
227222272235498321041183252320001178311549651103661032.0
22723227231636180184683881219930130491986381101647116.3
227242272413073810169421524620201182908164424181316580.1
227252272555825841070120313102000004477786345005594137.1
227262272665870881049231979920150148112454755076594705.0
227272272793192421039853910520141055954072789009321511.4
2272822728657978610892319721020001053582513411006584708.2
227292272982244181038867281920181062941291572068231424.8